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We present an image tag completion method, namely PMF-SVN, where the key idea is to exploit images' Semantically and Visually similar Neighborhoods (SVNs) in the learning process of a Probabilistic Matrix Factorization (PMF) framework. We propose a two-step SVN formation algorithm that can generate an image set with the images being both visually and semantically similar. Furthermore, we introduce an efficient way to incorporate the formed SVNs into the learning process of PMF, under thedoi:10.1145/2671188.2749333 dblp:conf/mir/Rafailidis15 fatcat:brktsovlpzgqflkcqqre7dl2xe